class: center, middle, title-slide .title[ # Garment Manufacturing and Women’s Work, Reproduction, and Human Capital ] .subtitle[ ## SM Shihab Siddiqui ] .author[ ### University of Oregon ] .date[ ### December, 2022 ] --- # Introduction Since 1960s, developing countries experienced: -- - Large .hi[declines in fertility] -- - Improvements in .hi[women's education] -- Can .hi[industrialization] friendly towards .hi[women's employment] explain these changes? -- `\(\uparrow\)` Opportunity costs in child-bearing and child-rearing -- `\(\uparrow\)` Returns to human capital in agrarian settings -- `\(\implies\)` Fertility reduction and increased human capital accumulation .footnote[ [1] *Ager et al (2020), Guldi and Rahman (2022), and Brown and Guinnane (2018)* shows that a similar dynamic played out during the second industrial revolution in US and Europe.] --- # Introduction Bangladesh started industrialization in 1980s through .hi[Garment manufacturing] -- - First .hi[non-farm employment opportunity] for millions of women -- - In a context of high fertility, low education, and non-coercive family planning program -- Allows me to examine the salience of industrialization `\(\rightarrow\)` `\(\uparrow\)` women's employment `\(\rightarrow\)` `\(\downarrow\)` fertility & `\(\uparrow\)` human capital accumulation of women. -- Specifically, I test the .hi[effect of women's employment opportunities in garment manufacturing on:] -- - **Female labor force participation (FLFP)** - Age-specific marriage and fertility rates - School enrollment, literacy, and years of schooling --- # Why do we care? .hi[Manufacturing- and export-led growth and development] is important. Especially relevant since: -- - Pre-mature de-industrialization in developing countries (Rodrik, 2016). -- - Prevalence of women in garments industry is decreasing in Bangladesh.<sup>1<sup> -- Adds to the literature focusing on .hi[trade and lives of workers] *(Autor et al (2013), Atkin (2016), Li (2018) and Autor et al (2019))*. .footnote[ [1] Along the lines of what happens as technology improves in a manufacturing sector (Tejani and Kucera, 2021).] --- # Related work .hi[Heath and Mobarak (2015, JDE)] finds that Bangladeshi garments industry: - Increased FLFP substantially, delayed marriage, and reduced fertility; and reduced women's education gap. -- - Individual data is from a 2009 survey of 1395 households in sixty villages in four regions with garments. -- - Relatively large distance between villages near and far from garments. Estimated treatment effect may reflect spatial pattern of development. -- - Factory data is from a 2014 survey of local individuals. -- In contrast, I use .hi[census data], and .hi[construct a detailed factory-level dataset] of nearly all exporting garment factories across all regions of Bangladesh between 1991-2006. --- # Preview of the paper ## Methods I estimate the long run effect of employment opportunities on FLFP, reproductive, and human capital accumulation choices by: -- - Using shift-share methods from the trade literature -- - Specifically, my .hi[identifying variation comes from differences in product specialization] within the garment industry only in regions that had garments in them. --- # Preview of the paper ## Results Employment opportunities in garment industry led to: -- - .hi[Substantial increases in FLFP], and especially industrial FLFP -- - .hi[Small decline in school] enrollment - No statistically significant changes in literacy rates or years of schooling -- - No changes in age-specific marriage and fertility rates. --- # Organization of the paper The rest of the presentation will: - Discuss the context and economic question in greater detail -- - Present the methodology and data -- - Present the findings -- - Conclude the paper --- class: inverse, middle, center # The Garments Industry and Women's Employment Oppurtunity --- # Garments industry expansion .pull-left[ <img src="data:image/png;base64,#jmp_pres1_files/figure-html/unnamed-chunk-1-1.svg" width="100%" style="display: block; margin: auto;" /> ] .pull-right[ - <font size="5"> Grew at .hi[11% per year] since 1991. ] -- .pull-right[ - Second largest exporter of garments in the world, and accounts for .hi[85% of Bangladesh's exports] in recent decades. ] -- .pull-right[ - Accounted for about .hi[33% of industrial employment] in 2017. </font> ] --- # Knit versus woven prodcuts Ready made garments account for .hi[more than 90%] of exports -- Two broad categories of products: -- .pull-left[ .hi[Knit: HS code 61] <img src="data:image/png;base64,#figures//knit1.png" width="55%" style="display: block; margin: auto auto auto 0;" /> Single yarn looped repeatedly Examples: Most sweaters, cotton T-shirts ] -- .pull-right[ .hi[Woven: HS code 62] <img src="data:image/png;base64,#figures//woven1.png" width="52%" style="display: block; margin: auto auto auto 0;" /> Multiple yarn criss-crossed over and under each other Examples: Shirts, jackets, pants. ] --- # .smaller[Knit versus woven specialization] <img src="data:image/png;base64,#figures//knitWov.png" width="65%" style="display: block; margin: auto;" /> -- - Producing woven is more energy and capital intensive, and commands about 10% higher per unit price (Sytsma, 2022). -- - Woven factories are larger, and employs more women. -- - Labor tasks overlap. --- # Garments and FLFP ## A long history Textile and affiliated industry always employed relatively more women across different time and place. -- - Mid 1800s England (Burnette, 2008), USA (Field-Hendrey, 1998); developed and developing countries 1981-2008 (Kucera and Tejani, 2014). -- - Women engaged in more spinning and knitting for centuries *(Virginia Postrel, Textiles and the Fabric of Civilization)* -- - Surveys find that as high as .hi[60% of workers] in exporting garments industry are women. --- # .smaller[Against the grain: Bangladeshi FLFP] <img src="data:image/png;base64,#jmp_pres1_files/figure-html/unnamed-chunk-5-1.svg" style="display: block; margin: auto;" /> - Among 20-24 year olds, FLFP was about 49% in 2015 (ADB 2016) --- # Conceptual framework .hi[Recap:] Substantial expansion of an exporting industry in the industrialization process that is favorable to women's work. -- - Clear prediction: .hi[FLFP will increase] -- Theoretically, FLFP, fertility, marriage, and schooling decisions are endogenous. -- - Labor market opportunities changes net benefits of marriage and fertility *(see Greenwood et al (2017) for a review).* -- - Human capital accumulation could increase or decrease depending on skill intensity of exports *(Atkin (2016), Li (2018))* -- - In turn, effects fertility along the Quality-Quantity trade-off. -- .hi[What happened to reproductive and human capital accumulation choices is an empirical questions!] --- class: inverse, middle, center # Empirical strategy --- # Research question The goal is to estimate the long-term effect of women's employment opportunities in garment manufacturing on: -- - Female labor force participation (FLFP) -- - Age-specific marriage and fertility rates -- - School enrollment, literacy, and years of schooling --- # Data sources - .hi[Outcome and control data] is obtained by aggregating individual-level data from the Bangladesh Census 1991 (10% sub-sample), 2001 (10% sub-sample), and 2011 (5% sub-sample) to sub-districts. -- - I created a novel .hi[Factory-level dataset] by combining four different datasets from Bangladesh Garments Manufacturing and Exporters Association (BGMEA) datasets with: -- - Factory locations, date of establishment, number of machines in the factory, and the type of product they produce (knit versus woven, or mixed). -- - Trade data from Comtrade (2022) --- # Identification challenge ## Why not simple OLS? $$ `\begin{equation} Y_{s,t} = \beta \space \text{Female Labor Demand}_{s,t} + Z_{s,t} \beta_z + v_{s,t} \end{equation}` $$ -- - `\(Y_{s,t}\)` is an outcome of interest in sub-district `\(s\)` at decade ending in year `\(t\)`. - `\(Z_{s,t}\)` is a vector of controls -- Two key issues: -- - .hi[Correlation between garment location and development] of a sub-district -- - Labor demand, or production, or exports from a sub-district is .hi[not directly observed] --- # Spatial spread of garments <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#figures/rmgspread.png" alt="Spread of garment factories between 1991-2006" width="75%" /> <p class="caption">Spread of garment factories between 1991-2006</p> </div> -- - Factory locations dependent on .hi[infrastructure quality] (kagy, 2014). -- - I also verify that proxies of infrastructure conditions explain much more of the variation in existence of factories in a sub-district. --- # .smaller[Garments and development] <img src="data:image/png;base64,#jmp_pres1_files/figure-html/unnamed-chunk-7-1.svg" style="display: block; margin: auto;" /> Location of garment factory closely follows the spatiotemporal pattern of development. -- I solve the endogeneity from omitted measures of development .hi[using several steps.] --- # First difference model `\begin{equation} \Delta Y_{s,t} = \beta \space \Delta \text{Female Labor Demand}_{s,t} + \delta_{t} + Z_{s,t-10} \beta_z + X_{s,t-10} \beta_x + \epsilon_{s,t} \end{equation}` - `\(\Delta Y_{s,t}\)` is the .hi[decadal change] in outcome variables in sub-district `\(s\)` over decade ending at year `\(t\)`. -- - `\(\delta_t\)` are .hi[census-year fixed-effects] - `\(Z_{s, t-10}\)` is a vector of .hi[start of decade values of controls common in all regressions:] - Three proxies of .hi[infrastructure conditions:] electrification rate, urbanization rate, and density - Two measures of .hi[demographic conditions:] share working age (15-64) population, and average years of education of working age population. --- # First difference model `\begin{equation} \Delta Y_{s,t} = \beta \space \Delta \text{Female Labor Demand}_{s,t} + \delta_{t} + Z_{s,t-10} \beta_z + X_{s,t-10} \beta_x + \epsilon_{s,t} \end{equation}` - `\(X_{s,t-10}\)` .hi[start of decade outcomes for males] other than in the cases of regressions corresponding to marriage and fertility rates. -- ## Sample restriction Sub-districts with and without factories are very different. -- - Sample period of 1991-2011 overlaps with large changes education, urbanization, and infrastructure. -- - .hi[I restrict the analysis only to sub-districts that had a factory by 2006] --- # Measuring labor demand Taking inspiration from Autor (2013), one candidate measure of `\(\Delta \text{Female Labor Demand}_{s,t}\)` is: `\begin{equation} \sum_{i=0}^{9} \alpha_{s,t-i}^{K} * \frac{\text{Export}_{BD,t-i}^{K}}{L_{s,t-i}} + \sum_{i=1}^{9} \alpha_{s,t-i}^{W} * \frac{\text{Export}_{BD,t-i}^{W}}{{L_{s,t-i}}} \\ \alpha_{s,t-i}^{K} = \frac{Machines_{s,t-i}^{K}}{Machines_{BD,t-i}^{K}}, \alpha_{s,t-i}^{W} = \frac{Machines_{s,t-i}^{W}}{Machines_{BD,t-i}^{W}} \end{equation}` -- - Apportions .hi[total knit (woven) exports] originating in Bangladesh .hi[to a sub-district based on that sub-district's share of national knit (woven) production capacity]. -- - Scales by population -- I call this measure `\(\Delta \text{Export Exposure}_{s,t}\)`. --- # Export exposure I use beginning of the decade values of: - `\(\alpha_{s,t-10}^{K}\)`, `\(\alpha_{s,t-10}^{W}\)`, and `\(L_{t-10}\)` to account for location-time specific omitted shocks. Thus, -- `\begin{align} \Delta \text{ Export Exposure}_{s,t} =& \alpha_{s,t-10}^{K} * \frac{\Delta \space \text{Export}_{BD,t}^{K}}{L_{t-10}} + \alpha_{s,t-10}^{W} * \frac{\Delta \space \text{Export}_{BD,t}^{W}}{L_{t-10}} \end{align}` --- # Regression model The regression model is: `\begin{equation} \Delta Y_{s,t} = \beta \space \Delta \text{Export Exposure}_{s,t} + \delta_{t} + Z_{s,t-10} \beta_z + X_{s,t-10} \beta_x + \epsilon_{s,t} \end{equation}` --- # Identifying assumption Adopting Goldsmith-Pinkham et al (2020), the key assumption is that the differences in knit and woven specialization do not *change* outcomes through confounders. That is: - .hi[Identifying assumption 1:] Extent of knit versus woven specialization in a sub-district is uncorrelated with the errors conditional on controls. -- - .hi[Identifying assumption 2:] FLFP responds similarly to woven and knit shocks. -- - .hi[Example of a violation:] Woven employs more women, so areas with more woven factories has increased presence of fertility control programs because there are more women. --- class: inverse, middle, center # Factory Data --- # Data challenge Need to estimate `\(\alpha_{s,1991}^{K}\)`, `\(\alpha_{s,2001}^{K}\)`, and `\(\alpha_{s,1991}^{W}, \alpha_{s,2001}^{K}\)` -- - Need data on factory establishment date, location, number of machines **in 1991 and 2001**, and whether they are a knit or woven factory. -- - Created new dataset with estimated number of machines in 1991 and 2001 using BGMEA directory 2000-01, and 2009-10 -- - Matched using unique BGMEA number, names, addresses, and phone numbers. -- - Matched 87% of factories in 2000-01 directory --- # Two stylized factory facts - .hi[Factory life-cycles:] virtually no exit till early 2000s, exits starts gaining steam between 2000-2010. -- - Factory exits are unlikely to lead to erroneous share estimates. --- # Two stylized factory facts <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#figures/facSizeDist.png" alt="Factory Size Distribution" width="70%" /> <p class="caption">Factory Size Distribution</p> </div> -- - Firm size distribution seems constant over 1991-2011 except at the top end. Suggestive of no major technological change. --- class: inverse, middle, center # Results --- # Correlates of Shares <div class="figure" style="text-align: center"> <p class="caption">Correlates of Shares</p><img src="data:image/png;base64,#figures/tab_conf.png" alt="Correlates of Shares" width="60%" /></div> -- - .hi[Shares in 91 and 2001 is not correlated with subsequent changes.] --- # Garments and FLFP `\begin{equation} \Delta Y_{s,t} = \beta \space \Delta \text{Export Exposure}_{s,t} + \delta_{t} + Z_{s,t-10} \beta_z + X_{s,t-10} \beta_x + \epsilon_{s,t} \end{equation}` -- Outcomes I consider are `\(\Delta FLFPR_{s,t}\)` and `\(\Delta FLFPR\text{-Ind}_{s,t}\)` for: -- - Working age women (Ages 15-64) -- - Women Ages 15-29 (most common in garments industry) -- - Women Ages 15-20 (has schooling and marriage implications) -- `\(\Delta \text{Export exposure}_{s,t}\)` is .hi[$1,000 per working age person] in a decade. --- # Garments and overall FLFP <img src="data:image/png;base64,#figures/tab_flfpALL.png" width="70%" style="display: block; margin: auto;" /> - Mean exposure of $854 in 2 decades, .hi[about 6-12 months of average income]. -- - `\(\uparrow\)` Overall FLFP by .hi[3.47 - 5.36 percentage points] -- - Roughly .hi[32% of the increases in FLFP] in areas with garment factories over 1991-2011. --- # Garments and Industrial-FLFP <img src="data:image/png;base64,#figures/tab_flfpIND.png" width="70%" style="display: block; margin: auto;" /> - Mean exposure of $854 in 2 decades, .hi[about 6-12 months of average income]. -- - `\(\uparrow\)` Overall FLFP by .hi[4.58 - 6.46 percentage points] -- - Roughly .hi[55% of the increases in industrial-FLFP] in areas with garment factories over 1991-2011. --- # .smaller[Discussions: Garments and FLFP] - Garments industry is a major contributor to increases in FLFP. -- - Greater effect on industrial FLFP -- - Greater effect for younger women. -- - Using a DiD estimator, .hi[Heath and Mobarak (2015)] found that women in villages within commuting zones of garment factories had about 15 percentage point more FLFP. -- - My magnitude is about half. --- # .smaller[Discussions: Garments and FLFP] <img src="data:image/png;base64,#figures/distance.png" width="40%" style="display: block; margin: auto;" /> - Garment factories are in urban regions -- - With .hi[50 percent] urbanization in my sample sub-districts, FLFP increases could be as high as 7-10.5 percentage points in a sample similar to Heath and Mubarak (2015) -- - .hi[Possibly, the effect of garments on Bangladeshi FLFP is smaller than previously thought, though it is quite substantial.] --- # .smaller[Discussions: Garments and FLFP] Autor et al. (2019) estimates suggest two decades of Chinese import competition reduced manufacturing employment as a share of population by 2.12 percentage points. -- - My estimate of effect of export exposure on FLFP is much larger, reflecting the relatively more important role of garment industry in the Bangladeshi FLFP context. --- # Garments and schooling `\begin{equation} \Delta Y_{s,t} = \beta \space \Delta \text{Export Exposure}_{s,t} + \delta_{t} + Z_{s,t-10} \beta_z + X_{s,t-10} \beta_x + \epsilon_{s,t} \end{equation}` Outcomes I consider are: - `\(\Delta \text{Enrollment}_{s,t}\)` of girls: -- - Ages 5-9, Ages 10-13 (tells us about changes in attitudes towards women's education) -- - Ages 14-19 (tells us about trade-offs in FLFP and education) -- - `\(\Delta \text{Literacy}_{s,t}\)` and `\(\Delta \text{Years of Schooling}_{s,t}\)` of girls ages 14-19 (tells us about overall effect) --- # Garments and enrollment <img src="data:image/png;base64,#figures/tab_enroll.png" width="90%" style="display: block; margin: auto;" /> - At the mean exposure value, enrollment of working age girls (14-19 year old) .hi[reduces by 2.71 percentage point] -- - No effect in enrollment of other ages. --- # .smaller[Garments and human capital accumulation] <img src="data:image/png;base64,#figures/tab_hc.png" width="70%" style="display: block; margin: auto;" /> -- - No effect on literacy -- - Not surprising, literacy is established earlier in school -- - Average years of schooling is negative and almost statistically significant **(t-value is -1.625)** --- # .smaller[Garments and human capital accumulation] - Slight `\(\downarrow\)` in human capital accumulation -- - .hi[Contradicts] the findings of Heath and Mubarak (2015) -- - Findings in line with: -- - Atkin (2016): Export manufacturing opportunity reduced school enrollment of older teenagers in 1980-2005 -- - Li (2018): Expansion of low (high) low-skill (high-skill) exports reduces (increases) schooling --- # .smaller[Garments and reproductive behavior] <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#figures/marfert.png" alt="Age specific ever-married and realized fertility rates" width="70%" /> <p class="caption">Age specific ever-married and realized fertility rates</p> </div> --- # .smaller[Garments and reproductive behavior] `\begin{equation} \Delta Y_{s,t} = \beta \space \Delta \text{Export Exposure}_{s,t} + \delta_{t} + Z_{s,t-10} \beta_z + \epsilon_{s,t} \end{equation}` Outcomes I consider are: -- - `\(\Delta \text{Marriage Rates}_{s,t}\)` for 15-20, and 21-30 years old (ages when employment at garments is most likely) -- - `\(\Delta \text{Fertility Rates}_{s,t}\)` for 21-30 (period of `\(\uparrow\)` opportunity cost of child-bearing and reading), and 30-40 years old (tests changes in timing of child-bearing). --- # Garments and marriage <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#figures/tab_marr.png" alt="Age specific ever-married and realized fertility rates" width="70%" /> <p class="caption">Age specific ever-married and realized fertility rates</p> </div> -- - No impact on marriage rates --- # Garments and fertility <img src="data:image/png;base64,#figures/tab_fert.png" width="70%" style="display: block; margin: auto;" /> -- - No impact on fertility rates --- # .smaller[Discussion: Garments and reproductive behavior] - No effect on marriage and fertility behavior -- - Heath and Mobarak (2015) finds very small effects in case of Bangladesh. -- - Autor (2019) finds small effects in case of US. -- - Bangladesh was going through a very fast fertility transition already. --- # Conclusion Bangladesh started its industrialization in late 1970s. -- - The garment industry played a dominant role. Moreover, -- - Millions of Bangladeshi women got their first non-farm employment opportunity. --- # Conclusions - I found that this increased opportunity did increase FLFP substantially -- - Reduced schooling for older girls, but without serious effect on overall human capital accumulation -- - No effect on fertility -- At least in the case of Bangladesh, and perhaps more generally, ongoing industrialization may not explain the rapid fertility transition and improvements in women's education. --- class: clear, center, middle # Thanks